Traffic based driving analysis

ABSTRACT

A driving analysis server may be configured to receive vehicle operation data from vehicle sensors and telematics devices of a first vehicle, and may use the data to identify a potentially high-risk or unsafe driving behavior by the first vehicle. The driving analysis server also may retrieve corresponding vehicle operation data from one or more other vehicles, and may compare the potentially high-risk or unsafe driving behavior of the first vehicle to corresponding driving behaviors in the other vehicles. A driver score for the first vehicle may be calculated or adjusted based on the comparison of the driving behavior in the first vehicle to the corresponding driving behaviors in the other vehicles.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a non-provisional of U.S. ProvisionalApplication No. 61/739,432, entitled “Traffic Based Driving Analysis,”filed Dec. 19, 2012, the contents of which are hereby incorporated byreference in their entirety for all purposes.

TECHNICAL FIELD

Aspects of the disclosure generally relate to the analysis of drivingdata and calculation of driver scores. In particular, various aspects ofthe disclosure include a framework for evaluating a driving behavior ata vehicle using vehicle sensor data and telematics from a plurality ofother vehicles and other data sources.

BACKGROUND

Telematics includes the use of technology to communicate informationfrom one location to another. Telematics has been used for variousapplications, including for the exchange of information with electronicsensors. As telematics technology has progressed, various communicationmethodologies have been incorporated into automobiles and other types ofvehicles.

Telematics systems such as on-board diagnostics (OBD) systems may beused in automobiles and other vehicles. OBD systems may provideinformation from the vehicle's on-board computers and sensors, allowingusers to monitor a wide variety of information relating to the vehiclesystems, such as engine RPM, emissions control, coolant temperature,vehicle speed, timing advance, throttle position, and oxygen sensing,and many other types of data. Telematics devices installed withinvehicles may be configured to access the vehicle computers and sensordata, and transmit the data to a display within the vehicle, a personalcomputer or mobile device, or to a centralized data processing system.Data obtained from OBD systems has been used for a variety of purposes,including maintenance, diagnosis, and analysis.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to methods, computer-readable media,and apparatuses for analyzing vehicle operation data, or driving data,and calculating or adjusting a driver score based on the analyzeddriving data. One or more on-board data recording systems within avehicle, for example, a telematics device, may be configured to collectvehicle operational data and transmit the data to a vehicle operationcomputer system or a driving analysis server. Vehicle operational datamay include various data collected by the vehicle's internal sensors,computers, and cameras, such as the vehicle's speed, rates ofacceleration or braking, instances of swerving, impacts to the vehicle,data indicating driver distractions within the vehicle, and usage ofseat belts, turn signals, and other vehicle controls. Based on thevehicle operational data, the driving analysis server may be configuredto identify one or more potentially high-risk or unsafe drivingbehaviors at a first vehicle, for example, speeding, sudden accelerationor braking, swerving, lane departures, tailgating, etc. According tocertain aspects, additional data may be retrieved from one or moreexternal data sources in order to identify the potentially high-risk orunsafe driving behaviors. For example, speeding by the first vehicle maybe determined using speed sensor data and location data from the firstvehicle, along with speed limit data from a separate data source. Asanother example, occurrences of aggressive driving in bad weather may beidentified using operation data from the vehicle, along with weatherdata from a separate weather database.

In accordance with further aspects of the present disclosure,corresponding vehicle operation data may be retrieved for one or moreadditional vehicles at a similar time, location, and/or circumstances tothe potentially high-risk or unsafe driving behavior of the firstvehicle. For example, if the potentially high-risk or unsafe drivingbehavior of the first vehicle is speeding along an isolated road, thencorresponding speed data may be retrieved from other vehicles along thesame isolated road to identify one or more other vehicles that also mayhave been speeding along the same road. The driving analysis server mayidentify and measure the number of occurrences of the same drivingbehavior to determine whether or not to adjust a driver score for thefirst vehicle based on the driving behavior. For instance, if a highpercentage of other vehicles have the same driving behavior as the firstvehicle, the behavior may be deemed less risky or unsafe and the driverscore associated with the first vehicle might not be lowered, whereas ifa low percentage of other vehicles have the same driving behavior, thebehavior may be deemed more risky or unsafe and the driver scoreassociated with the first vehicle may be lowered.

Other features and advantages of the disclosure will be apparent fromthe additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

FIG. 2 is a diagram of a driving analysis system, according to one ormore aspects of the disclosure.

FIG. 3 is a flow diagram illustrating an example method of adjusting adriver score based on the driving behavior of a first vehicle and one ormore other vehicles, according to one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a computer system, or a computer program product.Accordingly, those aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment combiningsoftware and hardware aspects. Furthermore, such aspects may take theform of a computer program product stored by one or morecomputer-readable storage media having computer-readable program code,or instructions, embodied in or on the storage media. Any suitablecomputer readable storage media may be utilized, including hard disks,CD-ROMs, optical storage devices, magnetic storage devices, and/or anycombination thereof. In addition, various signals representing data orevents as described herein may be transferred between a source and adestination in the form of electromagnetic waves traveling throughsignal-conducting media such as metal wires, optical fibers, and/orwireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a computing device (or system) 101in communication system 100 that may be used according to one or moreillustrative embodiments of the disclosure. The device 101 may have aprocessor 103 for controlling overall operation of the device 101 andits associated components, including RAM 105, ROM 107, input/outputmodule 109, and memory 115. The computing device 101, along with one ormore additional devices (e.g., terminals 141, 151) may correspond to anyof multiple systems or devices, such as a driving analysis server orsystem, configured as described herein for receiving and analyzingvehicle driving data and calculating driver scores based on the drivinganalysis.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 101 mayprovide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual and/or graphical output. Software may be stored withinmemory 115 and/or storage to provide instructions to processor 103 forenabling device 101 to perform various functions. For example, memory115 may store software used by the device 101, such as an operatingsystem 117, application programs 119, and an associated internaldatabase 121. Processor 103 and its associated components may allow thedriving analysis system 101 to execute a series of computer-readableinstructions to receive a driving data from a first vehicle, retrieveadditional driving data for other vehicles corresponding to firstvehicle driving data, and perform a driving data analysis for the firstvehicle.

The driving analysis system 101 may operate in a networked environment100 supporting connections to one or more remote computers, such asterminals 141 and 151. The terminals 141 and 151 may be personalcomputers, servers (e.g., web servers, database servers), or mobilecommunication devices (e.g., vehicle telematics devices, on-boardvehicle computers, mobile phones, portable computing devices, and thelike), and may include some or all of the elements described above withrespect to the driving analysis system 101. The network connectionsdepicted in FIG. 1 include a local area network (LAN) 125 and a widearea network (WAN) 129, and a wireless telecommunications network 133,but may also include other networks. When used in a LAN networkingenvironment, the driving analysis system 101 may be connected to the LAN125 through a network interface or adapter 123. When used in a WANnetworking environment, the system 101 may include a modem 127 or othermeans for establishing communications over the WAN 129, such as network131 (e.g., the Internet). When used in a wireless telecommunicationsnetwork 133, the system 101 may include one or more transceivers,digital signal processors, and additional circuitry and software forcommunicating with wireless computing devices 141 (e.g., mobile phones,vehicle telematics devices) via one or more network devices 135 (e.g.,base transceiver stations) in the wireless network 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computing devices and drivinganalysis system components described herein may be configured tocommunicate using any of these network protocols or technologies.

Additionally, one or more application programs 119 used by the drivinganalysis server/system 101 may include computer executable instructions(e.g., driving analysis programs and driver score algorithms) forreceiving vehicle driving data, retrieving additional driving data forother vehicles, analyzing and comparing the driving data with respect tospecific driving behaviors, performing a driving data analysis or driverscore computation for one or more vehicles or drivers, and performingother related functions as described herein.

As used herein, a driver score (or driving score) may refer to ameasurement of driving abilities, safe driving habits, and other driverinformation. A driver score may be a rating generated by an insurancecompany, financial instruction, or other organization, based on thedriver's age, vision, medical history, driving record, and/or otheraccount data relating to the driver. For example, an insurance companyserver 101 may periodically calculate driver scores for one or more ofthe insurance company's customers, and may use the driver scores toperform insurance analyses and determinations (e.g., determine coverage,calculate premiums and deductibles, award safe driver discounts, etc.).As discussed below, the driver score may be increased or decreased basedon the real-time data collected by on-board data recording systems(e.g., vehicle sensors, cameras, and telematics devices), and othersystems for measuring driving performance. For example, if a driverconsistently drives within posted speed limits, wears a seatbelt, andkeeps the vehicle in good repair, the driver score may be increased.Alternatively, if a driver regularly speeds, drives aggressively, anddoes not properly maintain the vehicle, the driver score may be lowered.It should be understood that a driver score, as used herein, may beassociated with an individual, group of individuals, or a vehicle. Forinstance, a family, group of friends or co-workers, or other group thatshares a vehicle, may have a single driver score that is shared by thegroup. Additionally, a vehicle may have an associated driver score thatis based on one or more primary drivers of the vehicle and can beaffected by the driving behavior of any the vehicle's drivers. In otherexamples, a vehicle may be configured to identify different drivers, andeach driver of the vehicle may have a separate driver score.

FIG. 2 is a diagram of an illustrative driving analysis system 200. Eachcomponent shown in FIG. 2 may be implemented in hardware, software, or acombination of the two. Additionally, each component of the drivinganalysis system 200 may include a computing device (or system) havingsome or all of the structural components described above for computingdevice 101.

The driving analysis system 200 shown in FIG. 2 includes a vehicle 210,such as an automobile, motorcycle, or other vehicle for which a drivinganalysis may be performed and for which a driver score may becalculated. The vehicle 210 may include one or more on-board datarecording systems, for example, on-board diagnostic (ODB) systems,telematics devices 216, and/or vehicle computer systems, which mayinclude or may be configured to communicate with vehicle sensors 212,proximity sensors and cameras 214, and other on-board data detectiondevices.

Vehicle operation sensors 212 refer to a set of sensors and datadetection devices capable of detecting and recording various conditionsat the vehicle and operational parameters of the vehicle. For example,sensors 212 may detect and store data corresponding to the vehicle'sspeed, distances driven, rates of acceleration or braking, and specificinstances of sudden acceleration, braking, and swerving. Sensors 212also may detect and store data received from the vehicle's 210 internalsystems, such as impact to the body of the vehicle, air bag deployment,headlights usage, brake light operation, door opening and closing, doorlocking and unlocking, cruise control usage, hazard lights usage,windshield wiper usage, horn usage, turn signal usage, seat belt usage,phone and radio usage within the vehicle, maintenance performed on thevehicle, and other data collected by the vehicle's computer systems.

Additional sensors 212 may detect and store the external drivingconditions, for example, external temperature, rain, snow, light levels,and sun position for driver visibility. Sensors 212 also may detect andstore data relating to moving violations and the observance of trafficsignals and signs by the vehicle 210. Additional sensors 212 may detectand store data relating to the maintenance of the vehicle 210, such asthe engine status, oil level, engine coolant temperature, odometerreading, the level of fuel in the fuel tank, engine revolutions perminute (RPMs), and/or tire pressure.

The vehicle 210 also may include one or more cameras and proximitysensors 214 capable of recording additional conditions inside or outsideof the vehicle 210. Internal cameras 214 may detect conditions such asthe number of the passengers in the vehicle 210, and potential sourcesof driver distraction within the vehicle (e.g., pets, phone usage,unsecured objects in the vehicle). External cameras and proximitysensors 214 may detect other nearby vehicles, traffic levels, roadconditions, traffic obstructions, animals, cyclists, pedestrians, andother conditions that may factor into a driving analysis.

The operational sensors 212 and the cameras and proximity sensors 214may store data within the vehicle 210, and/or may transmit the data toone or more external computer systems (e.g., a vehicle operationcomputer system 225 and/or a driving analysis server 220). As shown inFIG. 2, the operation sensors 212, and the cameras and proximity sensors214, may be configured to transmit data to a vehicle operation computersystem 225 via a telematics device 216. In other examples, one or moreof the operation sensors 212 and/or the cameras and proximity sensors214 may be configured to transmit data directly without using atelematics device 216. For example, telematics device 216 may beconfigured to receive and transmit data from operational sensors 212,while one or more cameras and proximity sensors 214 may be configured todirectly transmit data to a vehicle operation computer system 225 or adriving analysis server 220 without using the telematics device 216.Thus, telematics device 216 may be optional in certain embodiments whereone or more sensors or cameras 212 and 214 within the vehicle 210 may beconfigured to independently capture, store, and transmit vehicleoperation and driving data.

Telematics device 216 may be a computing device containing many or allof the hardware/software components as the computing device 101 depictedin FIG. 1. As discussed above, the telematics device 216 may receivevehicle operation and driving data from vehicle sensors 212, andproximity sensors and cameras 214, and may transmit the data to one ormore external computer systems (e.g., a vehicle operation computersystem 225 and/or a driving analysis server 220) over a wirelesstransmission network. Telematics device 216 also may be configured todetect or determine additional types of data relating to real-timedriving and the condition of the vehicle 210. In certain embodiments,the telematics device 216 may contain or may be integral with one ormore of the vehicle sensors 212 and proximity sensors and cameras 214discussed above, and/or with one or more additional sensors discussedbelow.

Additionally, the telematics device 216 may be configured to collectdata regarding the number of passengers and the types of passengers(e.g. adults, children, teenagers, pets, etc.) in the vehicle 210. Thetelematics device 216 also may be configured to collect data a driver'smovements or the condition of a driver. For example, the telematicsdevice 216 may include or communicate with sensors that monitor adriver's movements, such as the driver's eye position and/or headposition, etc. Additionally, the telematics device 216 may collect dataregarding the physical or mental state of the driver, such as fatigue orintoxication. The condition of the driver may be determined through themovements of the driver or through sensors, for example, sensors thatdetect the content of alcohol in the air or blood alcohol content of thedriver, such as a breathalyzer.

The telematics device 216 also may collect information regarding thedriver's route choice, whether the driver follows a given route, and toclassify the type of trip (e.g. commute, errand, new route, etc.). Incertain embodiments, the telematics device 216 may be configured tocommunicate with the sensors and/or cameras 212 and 214 to determinewhen and how often the vehicle 210 stays in a single lane or strays intoother lanes. To determine the vehicle's route, lane position, and otherdata, the telematics device 216 may include or may receive data from amobile telephone, a Global Positioning System (GPS), locational sensorspositioned inside a vehicle, or locational sensors or devices remotefrom the vehicle 210.

The telematics device 216 also may store the type of the vehicle 210,for example, the make, model, trim (or sub-model), year, and/or enginespecifications. The vehicle type may be programmed into the telematicsdevice 216 by a user or customer, determined by accessing a remotecomputer system, such as an insurance company or financial institutionserver, or may be determined from the vehicle itself (e.g., by accessingthe vehicle's 210 computer systems).

Vehicle operation computer system 225 may be a computing device separatefrom the vehicle 210, containing some or all of the hardware/softwarecomponents as the computing device 101 depicted in FIG. 1. The vehicleoperation computer system 225 may be configured to receive and store thevehicle operation data discussed above from vehicle 210, and similarvehicle operation data from one or more other vehicles 210 a-n. In theexample shown in FIG. 2, the vehicle operation computer system 225includes a vehicle operation database 227 that may be configured tostore the vehicle operation data collected from the vehicle sensors 212,proximity sensors and cameras 214, and telematics devices 216 of aplurality of vehicles. The vehicle operation database 227 may storeoperational sensor data, proximity sensor data, camera data (e.g.,image, audio, and/or video), location data and/or time data for multiplevehicles 210.

Data stored in the vehicle operation database 227 may be organized inany of several different manners. For example, a table in the vehicleoperation database 227 may contain all of the vehicle operation data fora specific vehicle 210, similar to a vehicle event log. Other tables inthe vehicle operation database 227 may store certain types of data formultiple vehicles. For instance, tables may store specific drivingbehaviors (e.g., driving speed, acceleration and braking rates,swerving, tailgating, use of turn signals or other vehicle controls,etc.) for multiples vehicles 210 at specific locations, such as specificneighborhoods, roads, or intersections. Vehicle operation data may alsobe organized by time, so that the driving behaviors of multiplesvehicles 210 may be stored or grouped by time (e.g., morning, afternoon,late night, rush hour, weekends, etc.) as well as location.

The system 200 also may include a driving analysis server 220,containing some or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. The driving analysis server 220may include hardware, software, and network components to receivevehicle operation data from the vehicle operation computer system 225and/or directly from a plurality of vehicles 210. The driving analysisserver 220 and the vehicle operation computer system 225 may beimplemented as a single server/system, or may be separateservers/systems. In some examples, the driving analysis server 220 maybe a central server configured to receive vehicle operation data from aplurality of remotely located vehicle operation computer systems 225.

As shown in FIG. 2, driving analysis server 220 may include a drivinganalysis module 221 and a driver score calculation module 222. Modules221 and 222 may be implemented in hardware and/or software configured toperform a set of specific functions within the driving analysis server220. For example, the driving analysis module 221 and the driver scorecalculation module 222 may include one or more driving analysis/driverscore calculation algorithms, which may be executed by one or moresoftware applications running on generic or specialized hardware withinthe driving analysis server 220. The driving analysis module 221 may usethe vehicle operation data received from the vehicle operation computersystem 225 and/or other systems to perform driving analyses for specificvehicles 210. The driver score calculation module 222 may use theresults of the driving analysis performed by module 221 to calculate oradjust a driver score for a driver of a vehicle 210 based on specificdriving behaviors. Further descriptions and examples of the algorithms,functions, and analyses that may be executed by the driving analysismodule 221 and the driver score calculation module 222 are describedbelow in reference to FIG. 3.

To perform driving analyses and driver score calculations, the drivinganalysis server 220 may initiate communication with and/or retrieve datafrom one or more vehicles 210, vehicle operation computer systems 225,and additional computer systems 231-233 storing data that may berelevant to the driving analyses and driver score calculations. Forexample, one or more traffic data storage systems 231, such as trafficdatabases, may store data corresponding to the amount of traffic andcertain traffic characteristics (e.g., amount of traffic, averagedriving speed, traffic speed distribution, and numbers and types ofaccidents, etc.) at various specific locations and times. One or moreweather data storage systems 232, such as weather databases, may storeweather data (e.g., rain, snow, sleet, hail, temperature, wind, roadconditions, visibility, etc.) at different locations and differenttimes. One or more additional driving databases/systems 233 may storeadditional driving data from one or more different data sources orproviders which may be relevant to the driving analyses and/or driverscore calculations performed by the driving analysis server 220.Additional driving databases/systems 233 may store data regarding eventssuch as road hazards and traffic accidents, downed trees, power outages,road construction zones, school zones, and natural disasters that mayaffect the driving analyses and/or driver score calculations performedby the driving analysis server 220. As discussed below in reference toFIG. 3, the driving analysis server 220 may retrieve and use data fromdatabases/systems 231-233 to analyze and evaluate the driving behaviorsof specific vehicles 210.

FIG. 3 is a flow diagram illustrating an example method of performing adriving analysis based on vehicle driving data. This example method maybe performed by one or more computing devices (e.g. driving analysisserver 220, vehicle operation computer system 225, and vehicletelematics device 216) in the driving analysis system 200.

The steps in the example method of FIG. 3 describe performing ananalysis to determine whether or not to adjust a driver score inresponse to a potentially high-risk or unsafe driving behavior (e.g.,speeding, sudden braking, swerving, tailgating, moving violations, etc.)based on similar driving behavior data from other vehicles. Forinstance, speeding may be considered a potentially high-risk or unsafedriving behavior, and drivers that speed frequently and excessively mayreceive lower driver scores than drivers that do not speed. However, ina particular flow of traffic, if most or all of the cars are speeding,then it might be safer for a driver to drive within the flow of trafficthan to drive at the speed limit. Similarly, an occurrence of suddenswerving or braking by a vehicle may indicate a high-risk or unsafedriving behavior by a driver not paying attention to the road. However,if many cars on the same road at or near the same time also brake orswerve suddenly, then the swerving or braking by these cars may indicatean unsafe road condition or obstruction (e.g., an icy road, fallen tree,disabled vehicle, etc.), rather than high-risk or unsafe driving by thedrivers whose vehicles were braking or swerving.

In step 301, a driving analysis server 220 may receive vehicle operationdata (or driving data) for a first vehicle 210. As described above inreference to FIG. 2, the driving analysis server 220 may receive vehicleoperation data from one or more vehicle operation computer systems 225and/or directly from telematics devices 216 or other systems on vehicles210. The first vehicle driving data may correspond to sensor datacollected by sensors 212, proximity or image data collected by sensorsand cameras 214, and/or additional data collected by a telematics device216 or other systems within a vehicle 210. In addition to the vehicleoperation data, the driving analysis server 220 may receive location andtime information corresponding to the vehicle operation data in step301. Vehicle location data and time data may be received from the samesources as other vehicle operation data, or may be collected bydifferent data sources or generated by the driving analysis server 220.For example, the driving analysis server 220 may receive vehicleoperation data from a vehicle operation system 225, and then mayinitiate communication with the vehicle's telematics device 216, GPSservers, time servers, or other systems to determine the location andtime that correspond to the received vehicle operation data.

In certain embodiments, telematics devices 216, vehicle operationsystems 225, and other data sources may transmit vehicle operation datafor a first vehicle 210 to the driving analysis server 220 in real-time(or near real-time). The driving analysis server 220 may be configuredto receive the vehicle operation data, and then perform real-time (ornear real-time) driving analyses and driver score calculations for thefirst vehicle 210. In other embodiments, vehicle operation data mightnot be transmitted in real-time but may be sent periodically (e.g.,hourly, daily, weekly, etc.) by telematics devices 216 or vehicleoperation systems 225. Periodic transmissions of vehicle operation datamay include data for a single vehicle or single driver, or for multiplevehicles or drivers. The driving analysis server 220 may be configuredto receive the periodic transmissions, and then to perform periodicdriving analyses and driver score calculations for one or more vehiclesand drivers.

In step 302, the driving analysis server 220 may identify one or morepotentially high-risk or unsafe driving behaviors within the operationdata of the vehicle 210. The driving behaviors identified in step 302may correspond to specific occurrences or patterns of high-risk, unsafe,or illegal driving activities that have the potential to affect thedriver score of the vehicle 210 or a driver of the vehicle 210.

For certain such driving behaviors, the driving analysis server 220 mayidentify the driving behavior by analyzing only the vehicle operationdata, for example, occurrences of sudden braking, accelerating,swerving, and tailgating. Other driving behaviors that may be identifiedbased only on the vehicle operation data include failure to useseatbelts, phone usage while driving, loud noise levels inside thevehicle while driving (e.g., high stereo volume or passenger noises), orother distractions in the vehicle (e.g., animated passengers or pets inthe vehicle, eating while driving, texting while driving, etc.).Additionally, impacts to the body of the vehicle 210 (e.g., minoraccidents, driving fast over speed bumps or dips, etc.) may beidentified based only on the operation data received for the vehicle210.

To identify other types of driving behaviors, the driving analysisserver 220 may analyze the vehicle operation data, as well as additionaldata retrieved from one or more external data sources. For example, toidentify an occurrence of speeding by the vehicle 210, the drivinganalysis server 220 may receive vehicle speed data from a telematicsdevice 216 or vehicle operation system 225, and may receive thevehicle's location from the telematics device 216, vehicle operationsystem 225, or a separate GPS system associated with the vehicle 210.Then, the driving analysis server 220 may access a speed limit databaseto determine the legal speed limit at the location of the vehicle 210,and may compare the speed limit to the detected speed of the vehicle.The driving analysis server 220 may identify other moving violationsusing similar techniques. For example, the driving analysis server 220may identify a failure to use proper turn signals by analyzing the turnsignal usage of the vehicle 210, as compared to the location/drivingroute of the vehicle. Stop sign violations, illegal turns, and U-turnviolations may be identified by comparing the driving route of thevehicle 210 to a database of the traffic regulations and posted trafficsigns at different streets and intersections along the driving route.

Additional driving behaviors that may be identified in step 302 includeoccurrences of risky or aggressive driving under adverse drivingconditions or in safe driving areas. For example, it may be deemedhigh-risk or unsafe to drive a vehicle 210 at the maximum speed limitduring a rainstorm, snowstorm, or on slick or icy road conditions. Todetect an occurrence of this type of driving behavior, the drivinganalysis server 220 may analyze the speed of the vehicle 210, the speedlimit at the vehicle's location, and the weather or road conditions atthe time the vehicle was being driven at that location. Additionally, itmay be deemed high-risk or unsafe to drive aggressively in safe drivingareas, such as construction zones and school zones. To identify anoccurrence of aggressive driving in a safe driving area, the drivinganalysis server 220 may analyze certain vehicle operation data (e.g.,sudden acceleration or braking, phone usage while, other driverdistractions, etc.), and compare the vehicle's location to a database ofsafe driving areas.

In step 303, the driving analysis server 220 may retrieve driving datafor one or more additional vehicles (e.g., vehicles 210 a-n)corresponding to the driving behavior(s) identified for the firstvehicle 210 in step 302. As discussed below, the data retrieved in step303 may allow the driving analysis server 220 to identify occurrences ofthe same potentially high-risk or unsafe driving behavior in othervehicles at similar times, locations, and/or circumstances. For example,if the driving behavior identified in step 302 is an occurrence ofspeeding by the first vehicle 210 on Route 1 at 2:00 pm on Saturday,then in step 303 the driving analysis server 220 may retrieve vehiclespeed data for a plurality of other vehicles 210 a-n that were driven onRoute 1 at 2:00 pm on Saturday. Thus, the driving behavior identifiedfor the first vehicle (e.g., speeding) can be compared to thecorresponding driving behaviors (e.g., speeding or not speeding) forother vehicles being driven at the same time and location, and under thesame circumstances.

In certain embodiments, the driving analysis server 220 may identify apotentially high-risk or unsafe driving behavior of the first vehicle210 in real-time or near real-time (in step 302), and may retrievecorresponding driving data for other vehicles near the first vehicle ator near the same time (in step 303). After identifying a potentiallyhigh-risk or unsafe driving behavior in the first vehicle, one or moreof the telematics devices 216 of the first vehicle, the vehicleoperation system 225, or the driving analysis server 220 may be used toidentify other vehicles 210 a-n in the immediate vicinity of the firstvehicle 210 at the time of the identified driving behavior. GPS systemsor proximity sensors within the vehicle 210 and the vehicles 210 a-n,and/or vehicle location data from other sources, also may be used toidentify the set of vehicles in the immediate vicinity of the firstvehicle 210 at the time of the driving behavior.

In some instances, the driving analysis server 220 may already have, ormay already be configured to receive, the corresponding data from theadditional nearby vehicles 210 a-n. For example, the driving analysisserver 220 may receive a stream or batch of vehicle operation data froma vehicle operation system 225 corresponding to all of the vehicles inthe same geographic area at the same time. Thus, the driving analysisserver 220 may receive the corresponding vehicle operation data for anynearby vehicles 210 a-n at the time same that it receives the data forthe first vehicle 210 in step 301. In other instances, the drivinganalysis server 220 may specifically request the information from thevehicle operation system 225 or from vehicle telematics devices 216, forany vehicles determined to be nearby the first vehicle 210 at the timeof the identified driving behavior. Thus, the driving analysis server220 may be configured to locate, initiate communication with, andrequest specific sensor/camera data from a vehicle operation system 225or from one or more vehicle telematics devices 216 corresponding to thenearby vehicles 210 a-n. For example, if the potentially high-risk orunsafe driving behavior is tailgating by the first vehicle 210, then thedriving analysis server 220 may identify other nearby vehicles 210 a-nat the time of the identified tailgating, and may specifically requestdata from the proximity sensors and external cameras 214 of the othervehicles 210 a-n that may be used to identify similar occurrences oftailgating by the other vehicles 210 a-n.

Depending on the time and location of the potentially high-risk orunsafe driving behavior by the first vehicle 210, the driving analysisserver 220 may be unable to locate a significant number of additionalnearby vehicles, or may be unable retrieve corresponding driving datafrom the nearby vehicles. For example, driving behaviors identified instep 302 may take place on remote and isolated roads, or nearby vehiclesmight not have the necessary sensors 212-214 and telematics devices 216to detect and transmit the corresponding driving data to the drivinganalysis server 220. In such cases, the driving analysis server 220 mayretrieve corresponding driving data from one or more other data sources.For example, the driving analysis server 220 may request correspondingtraffic data from traffic databases 231, including the amount oftraffic, average driving speed, lane changes, and instances of accidentsat times and locations corresponding to the driving behavior of thefirst vehicle 210.

When there is insufficient driving data available for other vehicles 210a-n at the time and location corresponding to the driving behavior ofthe first vehicle 210, the driving analysis server 220 may retrievesimilar data for different times and/or different locations that aresimilar to the time and location of the first vehicle's drivingbehavior. The specific type of data retrieved by the driving analysisserver 220 (e.g., the other vehicles 210 a-n identified, the specificsensor data used and other vehicle operation data retrieved, and thetimes and locations corresponding to the vehicle operation data) maydepend on the type of driving behavior identified for the first vehicle210 in step 302, and other specific circumstances relating to thedriving behavior.

For example, if the driving behavior identified for the first vehicle210 in step 302 is speeding along a particular section of Route 1 at2:00 am on Monday, then in step 303, the driving analysis server 220 mayretrieve corresponding vehicle speed data for any vehicles on the samesection of Route 1 between 12:00 am and 4:00 am, Monday through Friday.Depending on the amount of data available, speed data during this timewindow may be collected over a number of previous weeks or months toprovide an adequate sample size. As another example, if the drivingbehavior identified for the first vehicle 210 in step 302 is failure tostop completely at a stop sign at 2:00 pm on Tuesday afternoon, then instep 303, the driving analysis server 220 may retrieve correspondingvehicle data (e.g., driving speed and braking data) for any vehicles atthe same intersection at any time in the previous several months. Asanother example, if the driving behavior identified for the firstvehicle 210 in step 302 is a sudden swerve at a certain point on MainStreet at 4:30 pm on Wednesday, then in step 303, the driving analysisserver 220 may retrieve corresponding vehicle data (e.g.,steering/swerving data) only for vehicles on the same section of MainStreet, and only between 3:30 pm and 5:30 pm on the same Wednesdayafternoon. The sudden swerve in this example may be due to an accident,disabled vehicle, or temporary road obstruction, and therefore datamight not be collected for previous weeks and months because such datamight not accurately reflect the similar circumstances to the drivingbehavior identified for the first vehicle 210. As yet another example,if the driving behavior identified for the first vehicle 210 in step 302is driving aggressively during a rain or snow storm on Thursday morning,then in step 303, the driving analysis server 220 may retrievecorresponding vehicle data (e.g., speed, acceleration and braking rates,steering/swerving data) for vehicles within the same general area as therain or storm at any time during the storm.

In step 304, the driving analysis server 220 may use the additionalvehicle driving data retrieved in step 303 to identify occurrences ofthe same or similar driving behaviors as the first vehicle drivingbehavior that was identified in step 302. The driving analysis server220 may perform the analysis in step 304 separately for each othervehicle and for each driving behavior. That is, for each additionalvehicle that vehicle driving data was retrieved in step 303, the drivinganalysis server 220 may analyze the driving data for that vehicle todetermine if one or more instances occur for the same (or a similar)driving behavior. For example, if the driving behavior identified forthe first vehicle 210 in step 302 is speeding by greater than 20miles-per-hour (MPH) along a particular section of Main Street, then instep 304, the driving analysis server 220 may analyze each trip by eachother vehicle down the same section of Main Street to identify otheroccurrences of speeding greater than 20 MPH. If the data retrieved instep 303 includes vehicle driving data for 500 vehicle trips down thesame section of Main Street, then the driving analysis server 220 mayanalyze the vehicle's speed data on each trip and classify each of the500 trips as either an occurrence of speeding greater than 20 MPH or anoccurrence of not speeding greater than 20 MPH.

When identifying occurrences of the same or similar driving behaviors,between the first vehicle 210 and the additional vehicles 210 a-210 n,the driving analysis server 220 may compare the specific vehicleoperational data (e.g., vehicle speeds, rates of accelerating andbraking, severity of swerving, tailgating distances, number and severityof lane departures, etc.) between the vehicles. In certain instances,thresholds may be defined for each driving behavior (e.g., speeding,sudden acceleration and braking, swerving, tailgating, other movingviolations, etc.) so that occurrences and non-occurrences of the drivingbehavior can be identified within the data retrieved in step 303. Thethresholds may depend on the general type of driving behavior as well asspecific situational factors. For example, an occurrence of speeding forone analysis in step 304 may be defined 10 miles-per-hour (MPH) over theposted speed limit, whereas in a different analysis in step 304 (e.g.,on a different road, with different road conditions, and at a differenttime of day) may define speed as 5 MPH over the posted limit. Thethresholds for occurrences of sudden accelerating, braking, andswerving, and failing to use turn signals (or other vehicle controls),may be based on the readings of the vehicle's operational sensors 212.The thresholds for occurrences of tailgating may be based on thevehicle's proximity sensors and cameras 214. For other potentiallyhigh-risk or unsafe driving behaviors, the appropriate sensor data orother data sources may be used to set and use thresholds for measuringoccurrences and non-occurrences of the behavior by other vehicles 210a-n.

The data retrieved in step 303 and analyzed in step 304 may correspondto the same location and time as the identified driving behavior of thefirst vehicle 210 (e.g., Main Street at 1st Avenue, 1:00 pm on Monday),or may correspond to different locations (e.g., Broad Street at 1stAvenue, 1:00 pm on Monday) and/or different times (Main Street at 1stAvenue, 1:00 pm on Tuesday). For each of these examples, the drivinganalysis server 220 may identify occurrence or a non-occurrence of thesame driving behavior identified in step 302. However, the thresholdsfor identifying an occurrence or a non-occurrence may be adjusted whenthe analyzed data corresponds to different times and/or locations. Forexample, if the first vehicle 210 was speeding down a quiet isolatedroad at night, and the data analyzed in step 304 includes vehicle speeddata for the same road during both daytime at nighttime, then thedriving analysis server 220 may set a higher MPH threshold foridentifying an occurrence of speeding during the daytime because of theincreased daytime visibility.

In step 305, the driving analysis server 220 determines if the number(or percentage) of occurrences of the driving behavior identified instep 304 exceeds a threshold number (or threshold percentage) ofoccurrences. If the number/percentage does not exceed the thresholdnumber/percentage (305:No), this indicates a relatively small number ofoccurrences of the potentially high-risk or unsafe driving behavioramong the data retrieved in step 303. In this case, the potentiallyhigh-risk or unsafe driving behavior identified for the first vehicle210 in step 302 is a relatively uncommon driving behavior. Accordingly,in step 306, the driving analysis server 220 may adjust (e.g., lower) adriver score associated with the first vehicle or a driver of the firstvehicle, based on the potentially high-risk or unsafe driving behavioridentified in step 302. On the other hand, if the number/percentageexceeds the threshold number/percentage (305:Yes), this indicates arelatively large number of occurrences of the potentially high-risk orunsafe driving behavior among the data retrieved in step 303. In thiscase, the driving behavior identified for the first vehicle 210 in step302 is a relatively common driving behavior, and may be less likely tobe a high-risk or unsafe driving behavior. Accordingly, in step 307, thedriving analysis server 220 might not adjust the driver score associatedwith the first vehicle or driver of the first vehicle, based on thedriving behavior identified in step 302.

As an example, if the potentially high-risk or unsafe driving behavioridentified for the first vehicle 210 in step 302 is speeding by greaterthan 10 MPH, then in step 305, the driving analysis server 220 maycompare the percentage of other vehicles that were also speeding bygreater than 10 MPH against a percentage threshold (e.g., 50%). In thisexample, if 90% of the other vehicles (for which data was retrieved instep 303) were also speeding by greater than 10 MPH, then the driverscore associated with the first vehicle 210 will not be adjusted (aftercomparing the 90% occurrence rate to the 50% threshold) because thedriving analysis server 220 may conclude that the same driving behavioris common among other vehicles. On the other hand, if only 5% of theother vehicles were speeding by greater than 10 MPH, than the driverscore will be adjusted (after comparing the 5% occurrence rate to the50% threshold), because the driving analysis server 220 may concludethat the first vehicle 210 was speeding excessively in comparison withother vehicles. Similar analysis and threshold comparisons may beperformed for other types of potentially high-risk or unsafe drivingbehavior, such as sudden acceleration or braking, swerving, tailgating,lane departures, failure to use turn signals or other vehicle controls,or other moving violations. In these examples, if a relatively highpercentage of other vehicles have the same driving behavior, thebehavior may be deemed less risky or unsafe and the driver scoreassociated with the first vehicle 210 might not be lowered, or it may belowered by less than it otherwise would be. If a relatively lowpercentage of other vehicles have the same driving behavior, thebehavior may be deemed more risky or unsafe and the driver scoreassociated with the first vehicle 210 may be lowered, or it may belowered by more than it otherwise would be.

In certain examples, the driving analysis server 220, vehicle operationcomputer system 225, and/or the telematics device 216 may be configuredto send data to one or more output devices that may be visible to thedriver or other users. For example, the telematics device 216 or vehicleoperation computer system 225 may send vehicle operation data and anyidentified occurrences of potentially high-risk or unsafe drivingbehavior to a separate computing device (e.g., personal computer, e-mailaccount, mobile device) associated with the drivers or the owners of thevehicle 210. Additionally, the driving analysis server 220 may beconfigured to notify vehicle drivers or owners of any adjustments madeto the driver's/vehicle's driver score in step 306. Any occurrences ofidentified driving behaviors that may affect the driver score and/or anyadjustments to the driver score also may be displayed within the vehicle210. For example, driving analysis server 220, vehicle operationcomputer system 225, and/or the telematics device 216 may be configuredto project a warning of a potentially high-risk or unsafe drivingbehavior, or an updated driver score, on the display console,windshield, or rear view mirror of the vehicle 210.

As discussed above in step 304, the driving analysis server 220 mayanalyze the corresponding vehicle driving data in step 304 and mayclassify each vehicle and driving behavior as either an occurrence or anon-occurrence of the first vehicle driving behavior identified in step302. However, in other examples, the driving analysis server 220 neednot classify each vehicle/driving behavior as an occurrence or anon-occurrence, but instead may perform a statistical analysis on thecomplete set of corresponding vehicle driving data. In such examples,driving analysis server 220 may determine a statistical distribution (orstatistical function) representing the driving behavior, and then mayplace the first vehicle driving behavior within the statisticaldistribution or function. For instance, after determining a statisticaldistribution for the driving behavior, the driving analysis server 220may determine that the identified behavior of the first vehicle 210corresponds to the Nth percentile among the corresponding vehicledriving data retrieved in step 303. In these examples, the threshold instep 305 may correspond to a percentile threshold (e.g., 30th percentile50th percentile, 75th percentile, 98th percentile, etc.) for the drivingbehavior of the first vehicle 210. If the magnitude or severity of thepotentially high-risk or unsafe driving behavior is greater than thepercentile threshold, than the driver score for the vehicle/driver maybe adjusted (step 306), and if the magnitude or severity of the drivingbehavior is not greater than the percentile threshold, than the driverscore for the vehicle/driver may not be adjusted (step 307).

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

The invention claimed is:
 1. A driving analysis server comprising: a processing unit comprising a processor; and a memory unit storing computer-executable instructions, which when executed by the processing unit, cause the driving analysis server to: receive first vehicle driving data collected by vehicle operation sensors within a first vehicle at a first vehicle location and a first vehicle time; analyze the received first vehicle driving data to determine a first driving behavior of a first driver of the first vehicle; calculate a driver score for the first driver of the first vehicle based on data received from the vehicle operation sensors within the first vehicle; receive other vehicle driving data from one or more driving data sources, the other vehicle driving data corresponding to one or more other vehicles driven at or near the first vehicle location at or near the first vehicle time; compare the first vehicle driving data to the other vehicle driving data corresponding to the one or more other vehicles driven at or near the first vehicle location at or near the first vehicle time to determine whether the first driving behavior is present in the other vehicle driving data, wherein comparing includes: identifying one or more occurrences of the first driving behavior within the other vehicle driving data; comparing a measurement of the occurrences of the first driving behavior within the other vehicle driving data to a threshold value; responsive to determining that the measurement of the occurrences of the first driving behavior within the other vehicle driving data is greater than the threshold value, maintaining the driver score of the first driver of the first vehicle; responsive to determining that the measurement of the occurrences of the first driving behavior within the other vehicle driving data is below the threshold value, adjusting the driver score of the first driver of the first vehicle; responsive to determining that the first driving behavior is not present in the other vehicle driving data, adjusting a driver score of the first driver of the first vehicle; and responsive to determining that the first driving behavior is present in the other vehicle driving data, maintaining the driver score of the first driver of the first vehicle; and output the driver score.
 2. The driving analysis server of claim 1, wherein the first driving behavior comprises at least one of: an occurrence of sudden acceleration, an occurrence of sudden braking, an occurrence of speeding, an occurrence of swerving, an occurrence of an impact to the vehicle, an occurrence of tailgating, an occurrence of a lane departure, or an occurrence of a moving violation.
 3. The driving analysis server of claim 1, wherein the received first vehicle driving data comprises at least one of speed, acceleration, braking, steering, use of turn signals, use of seat belts, use of a radio, or use of a phone.
 4. The driving analysis server of claim 1, wherein the received first vehicle driving data comprises at least one of image data, video data, or object proximity data collected by at least one camera or proximity sensor in the first vehicle.
 5. The driving analysis server of claim 1, wherein receiving the other vehicle driving data comprises: determining the first vehicle location of the first vehicle; identifying the one or more other vehicles based on a determination that each of the one or more other vehicles are at or near the first vehicle location at or near the first vehicle time; and retrieving vehicle driving data collected by vehicle operation sensors within the one or more other vehicles.
 6. The driving analysis server of claim 1, wherein receiving the other vehicle driving data comprises: determining the first vehicle location of the first vehicle; retrieving vehicle driving data from a traffic database for vehicles that were driven at or near the first vehicle location at or near the first vehicle time.
 7. A method comprising: receiving, by a computing device, first vehicle driving data collected by vehicle operation sensors within a first vehicle at a first vehicle location at a first vehicle time; analyzing, by the computing device, the received first vehicle driving data to determine a first driving behavior of a first driver of the first vehicle; calculating, by the computing device, a driver score for the first driver of the first vehicle based on data received from the vehicle operation sensors within the vehicle; receiving, by the computing device, other vehicle driving data from one or more driving data sources, the other vehicle driving data corresponding to one or more other vehicles driven at or near the first vehicle location at or near the first vehicle time; comparing, by the computing device, the first vehicle driving data to the other vehicle driving data corresponding to the one or more other vehicles driven at or near the first vehicle location at or near the first vehicle time to determine whether the first driving behavior is present in the other vehicle driving data, wherein comparing includes: identifying one or more occurrences of the first driving behavior within the other vehicle driving data; comparing a measurement of the occurrences of the first driving behavior within the other vehicle driving data to a threshold value; responsive to determining that the measurement of the occurrences of the first driving behavior within the other vehicle driving data is greater than the threshold value, maintaining the driver score of the first driver of the first vehicle; responsive to determining that the measurement of the occurrences of the first driving behavior within the other vehicle driving data is below the threshold value, adjusting the driver score of the first driver of the first vehicle; responsive to determining that the first driving behavior is not present in the other vehicle driving data, adjusting a driver score of the first driver of the first vehicle; responsive to determining that the first driving behavior is present in the other vehicle driving data, maintaining the driver score of the first; and displaying the driver score.
 8. The method of claim 7, wherein the first driving behavior comprises at least one of: an occurrence of sudden acceleration, an occurrence of sudden braking, an occurrence of speeding, an occurrence of swerving, an occurrence of an impact to the vehicle, an occurrence of tailgating, an occurrence of a lane departure, or an occurrence of a moving violation.
 9. The method of claim 7, wherein the received first vehicle driving data comprises at least one of speed, acceleration, braking, steering, use of turn signals, use of seat belts, use of radio, or use of a phone.
 10. The method of claim 7, wherein the received first vehicle driving data comprises at least one of image data, video data, or object proximity data collected by at least one camera or proximity sensor in the first vehicle.
 11. The method of claim 7, wherein receiving the other vehicle driving data comprises: determining the first vehicle location of the first vehicle; identifying the one or more other vehicles based on a determination that each of the one or more other vehicles are at or near the first vehicle location at or near the first vehicle time; and retrieving vehicle driving data collected by vehicle operation sensors within the one or more other vehicles.
 12. The method of claim 7, wherein receiving the other vehicle driving data comprises: determining the first vehicle location of the first vehicle; retrieving vehicle driving data from a traffic database for vehicles that were driven at or near the first vehicle location at or near the first vehicle time.
 13. A driving analysis system comprising: a vehicle operation computer system comprising a processor and a memory unit storing computer-executable instructions, the vehicle operation computer system configured to receive and store vehicle operation data from a plurality of vehicle on-board data recording systems in a plurality of vehicles; and a driving analysis server comprising a processor and a memory unit storing computer-executable instructions, which when executed by the processor, cause the driving analysis server to: receive first vehicle driving data from the vehicle operation computer system, the first vehicle driving data corresponding to a first vehicle at a first vehicle location at a first vehicle time; analyze the received first vehicle driving data to determine a first driving behavior of a first driver of the first vehicle; calculate driver score for the first driver of the first vehicle based on data received from the vehicle operation sensors within the vehicle using the first vehicle location and the first vehicle time, retrieve other vehicle driving data corresponding to one or more other vehicles driven at or near the first vehicle location at or near the first vehicle time; compare the first vehicle driving data to the other vehicle driving data corresponding to the one or more other vehicles driven at or near the first vehicle location at or near the first vehicle time to determine whether the first driving behavior is present in the other vehicle driving data, wherein comparing includes: identifying one or more occurrences of the first driving behavior within the other vehicle driving data; comparing a measurement of the occurrences of the first driving behavior within the other vehicle driving data to a threshold value; responsive to determining that the measurement of the occurrences of the first driving behavior within the other vehicle driving data is greater than the threshold value, maintain the driver score of the first driver of the first vehicle; responsive to determining that the measurement of the occurrences of the first driving behavior within the other vehicle driving data is below the threshold value, adjust the driver score of the first driver of the first vehicle; responsive to determining that the first driving behavior is not present in the other vehicle driving data, adjust a driver score of the first driver of the first vehicle; responsive to determining that the first driving behavior is present in the other vehicle driving data, maintain the driver score of the first driver of the first; and display the driver score.
 14. The driving analysis system of claim 13, wherein identifying one or more occurrences of the first driving behavior within the other vehicle driving data comprises: accessing the vehicle operation computer system to identify the other vehicles at or near the first vehicle location at or near the first vehicle time; and determining occurrences of the first driving behavior in the other vehicles at or near the first vehicle location at or near the first vehicle time.
 15. The driving analysis system of claim 13, wherein identifying one or more occurrences of the first driving behavior within the other vehicle driving data comprises: accessing a traffic data storage system configured to receive and store traffic data regarding a plurality of driving behaviors, a plurality of geographic locations associated with said driving behaviors, and a plurality of time periods associated with said driving behaviors; and querying the traffic data storage system to determine occurrences of the first driving behavior at or near the first vehicle location at or near the first vehicle time. 